A reversed phase liquid chromatographic (RPLC) method was developed to simultaneously detect and quantify creatinine, quinolinic acid, gentisic acid and 4-hydroxybenzoic acid in urine. These four bio-markers are present in relatively high concentrations in urine. Using a 5% methanol in water mobile phase with 0.6% acetic acid and a Zorbax C column, baseline resolution for all four biomarkers in synthetic urine was achieved. Better resolution was obtained for the separation of these four compounds when water rich mobile phases were used. Detection of the four biomarkers in urine using the proposed RPLC method is limited by background from the urine matrix for the later eluting compounds and from the dead marker for earlier eluting compounds.

Alternate least squares (ALS) reconstructions of the infrared (IR) spectra of the individual layers from original automotive paint were analyzed using machine learning methods to improve both the accuracy and speed of a forensic automotive paint examination. Twenty-six original equipment manufacturer (OEM) paints from vehicles sold in North America between 2000 and 2006 served as a test bed to validate the ALS procedure developed in a previous study for the spectral reconstruction of each layer from IR line maps of cross-sectioned OEM paint samples. An examination of the IR spectra from an in-house library (collected with a high-pressure transmission diamond cell) and the ALS reconstructed IR spectra of the same paint samples (obtained at ambient pressure using an IR transmission microscope equipped with a BaF2 cell) showed large peak shifts (approximately 10 cm−1) with some vibrational modes in many samples comprising the cohort. These peak shifts are attributed to differences in the residual polarization of the IR beam of the transmission IR microscope and the IR spectrometer used to collect the in-house IR spectral library. To solve the problem of frequency shifts encountered with some vibrational modes, IR spectra from the in-house spectral library and the IR microscope were transformed using a correction algorithm previously developed by our laboratory to simulate ATR spectra collected on an iS-50 FT-IR spectrometer. Applying this correction algorithm to both the ALS reconstructed spectra and in-house IR library spectra, the large peak shifts previously encountered with some vibrational modes were successfully mitigated. Using machine learning methods to identify the manufacturer and the assembly plant of the vehicle from which the OEM paint sample originated, each of the twenty-six cross-sectioned automotive paint samples was correctly classified as to the “make” and model of the vehicle and was also matched to the correct paint sample in the in-house IR spectral library.

Swellable polymer microspheres that respond to pH were prepared by free radical dispersion polymerization using N-isopropylacrylamide (NIPA), N,N′-methylenebisacrylamide (MBA), 2,2-dimethoxy-2-phenylacetylphenone, N-tert-butylacrylamide (NTBA), and a pH-sensitive functional comonomer (acrylic acid, methacrylic acid, ethacrylic acid, or propacrylic acid). The diameter of the microspheres was between 0.5 and 1.0 μm. These microspheres were cast into hydrogel membranes prepared by mixing the pH-sensitive swellable polymer particles with aqueous polyvinyl alcohol (PVA) solutions followed by crosslinking with glutaric dialdehyde for use as pH sensors. Large changes in the turbidity of the PVA membrane were observed as the pH of the buffer solution in contact with the membrane was varied. These changes were monitored by UV–visible absorbance spectroscopy. Polymer swelling of many NIPA copolymers was reversible and independent of the ionic strength of the buffer solution in contact with the membrane. Both the degree of swelling and the apparent pKa of the polymer microspheres increased with temperature. Furthermore, the apparent pKa of the polymer particles could be tuned to respond sharply to pH in a broad range (pH 4.0–7.0) by varying the amount of crosslinker (MBA) and transition temperature modifier (NTBA), and the amount, pKa, and hydrophobicity of the pH-sensitive functional comonomer (alkyl acrylic acid) used in the formulation. Potential applications of these polymer particles include fiber optic pH sensing where the pH-sensitive material can be immobilized on the distol end of an optical fiber.

Swellable polymers that respond to pH (including a portion of the physiological pH range) have been prepared from N-isopropylacrylamide (NIPA) copolymerized with acrylic acid, methacrylic acid, ethacrylic acid or propacrylic acid by dispersion polymerization. When the swellable polymer particles are dispersed in a polyvinyl alcohol (PVA) hydrogel membrane, large changes occur in the turbidity of the membrane (which is measured using an absorbance spectrometer) as the pH of the buffer solution in contact with the hydrogel membrane is varied. The swelling of the NIPA copolymer is nonionic, as the ionic strength of the buffer solution in contact with the PVA membrane was increased from 0.1 to 1.0 M without a decrease in the swelling. For many of these NIPA copolymers, swelling was also reversible in both low- and high ionic strength pH-buffered media and at ambient and physiological temperatures. The composition of the formulation used to prepare these copolymers of NIPA can be correlated to the enthalpy and entropy of the pH-induced swelling.

A new method to determine the make and model of a vehicle from an automotive paint sample recovered at the crime scene of a vehicle-related fatality such as a hit-and-run using Raman microscopy has been developed. Raman spectra were collected from 118 automotive paint samples from six General Motors (GM) vehicle assembly plants to investigate the discrimination power of Raman spectroscopy for automotive clearcoats using a genetic algorithm for pattern recognition that incorporates model inference and sample error in the variable selection process. Each vehicle assembly plant pertained to a specific vehicle model. The spectral region between 1802 and 697 cm–1 was found to be supportive of the discrimination of these six GM assembly plants. By comparison, only one of the six automotive assembly plants could be differentiated from the other five assembly plants using Fourier transform infrared spectroscopy (FT-IR), which is the most widely used analytical method for the examination of automotive paint) and the genetic algorithm for pattern recognition. The results of this study indicate that Raman spectroscopy in combination with pattern recognition methods offers distinct advantages over FT-IR for the identification and discrimination of automotive clearcoats.